The present invention relates to a system and a process for estimating a vehicle body speed and/or a road surface friction coefficient of a wheeled vehicle.
Estimation of vehicle body speed and road surface friction coefficient is required in a anti-skid brake control system (or wheel slip brake control system) for automatically controlling the braking force to prevent wheel locking on braking, a traction control system for preventing the spinning of drive wheels due to excess driving power and a vehicle directional behavior control system for reducing a deviation of an actual vehicle motion variable such as a vehicle yaw rate from a desired value by controlling the braking forces of left and right wheels individually. The following are some conventional examples for estimating the vehicle body speed or friction condition. Some are put to practical use.
As to the estimation of the vehicle body speed, there are at least three conventional examples.
The first conventional example is often used in a traction control system for a two wheel drive vehicle. The non-driving wheels of the two wheel drive vehicle are exempt from acceleration slip (or driving slip). Therefore, this conventional example regards the wheel speed of the non-driving wheels as the vehicle body speed during the traction control operation.
The second conventional example is for an anti-skid brake control system. The system first eliminates a slip by temporarily removing the braking force of a specified wheel and then takes the wheel speed of the specified wheel as the vehicle body speed.
The third conventional example is arranged to sense the longitudinal acceleration of the vehicle, and determine the vehicle body speed by integrating the sensed longitudinal acceleration.
As to the estimation of the friction coefficient, there are known three conventional examples.
The first conventional example is disclosed in Japanese Patent Provisional Publication No. 7-132787. This system applies a light braking force on a specified wheel to detect a relationship between the braking force and slip during the light braking, and estimates the road surface friction coefficient by predicting a variation characteristic of the friction coefficient with respect to the slip rate.
The second conventional example is disclosed in Japanese Patent Provisional Publication No. 6-286630. This system is arranged to determine a relationship between the road surface friction coefficient and a certain sensed variable strongly correlated to the friction coefficient by learning with a neural network, and estimate the friction coefficient from the sensed variable during movement of the vehicle.
The third conventional example is disclosed in Japanese Patent Provisional Publication No. 7-101258. This system is arranged to determine a driving torque difference between the left and right drive wheels from a differential limiting torque of a limited slip differential, and to estimate the road friction coefficient according to a predicted characteristic of the road surface friction coefficient with respect to the slip rate, predicted from the torque difference and a wheel speed difference between the left and right drive wheels.
However, the first conventional vehicle body speed estimating technique is not applicable to a four wheel drive vehicle having no non-driving wheels. Moreover, this technique is unable to estimate the vehicle body speed during braking because the brakes are applied to all the wheels and none of the wheel speeds represents the vehicle body speed.
In the second conventional body speed estimating technique, the removal of the braking force to the specified wheel tend to incur undesired hunting and make the estimate of vehicle body speed unstable. As a result, the anti-skid control based on the estimate is less stable, and the control accuracy is poorer.
In the third conventional body speed estimating technique, a drift of the G sensor for sensing the vehicle longitudinal acceleration lowers the accuracy of the vehicle body speed estimation, and the road surface inclination exerts undesirable influence on the output of the G sensor.
The first conventional friction estimating technique is unable to estimate the friction coefficient during brake application to all the wheels due to lack of ability of predicting a variation of the road friction coefficient with respect to the slip rate.
The second conventional friction estimating technique requires a long time for the learning operation of the neural network and the design of the neural network architecture is not easy.
The third conventional friction estimating technique is incompetent for estimation of the friction coefficient during braking, and hence the application is limited. This technique takes no account of load transfer during vehicle motion, and this affects the estimation accuracy.